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The generic object detection (GOD) task has been successfully tackled byrecent deep neural networks, trained by an avalanche of annotated trainingsamples from some common classes. However, it is still non-trivial togeneralize these object detectors to the novel long-tailed object classes,which have only few labeled training samples. To this end, the Few-Shot ObjectDetection (FSOD) has been topical recently, as it mimics the humans' ability oflearning to learn, and intelligently transfers the learned generic objectknowledge from the common heavy-tailed, to the novel long-tailed objectclasses. Especially, the research in this emerging field has been flourishingin recent years with various benchmarks, backbones, and methodologies proposed.To review these FSOD works, there are several insightful FSOD survey articles[58, 59, 74, 78] that systematically study and compare them as the groups offine-tuning/transfer learning, and meta-learning methods. In contrast, wereview the existing FSOD algorithms from a new perspective under a new taxonomybased on their contributions, i.e., data-oriented, model-oriented, andalgorithm-oriented. Thus, a comprehensive survey with performance comparison isconducted on recent achievements of FSOD. Furthermore, we also analyze thetechnical challenges, the merits and demerits of these methods, and envisionthe future directions of FSOD. Specifically, we give an overview of FSOD,including the problem definition, common datasets, and evaluation protocols.The taxonomy is then proposed that groups FSOD methods into three types.Following this taxonomy, we provide a systematic review of the advances inFSOD. Finally, further discussions on performance, challenges, and futuredirections are presented.

Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison